AI/ML MODEL TEST MECHANISM
    4.
    发明申请

    公开(公告)号:US20250070902A1

    公开(公告)日:2025-02-27

    申请号:US18798373

    申请日:2024-08-08

    Abstract: Example embodiments of the present disclosure are related to artificial intelligence/machine learning (AI/ML) model test. A first apparatus transmits test configuration information to a second apparatus, the test configuration information indicating a test mode of an AI/ML model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator. The first apparatus receives, from the second apparatus, at least one predicted channel indicator for the at least one TRP, the at least one predicted channel indicator being derived by the second apparatus using the AI/ML model. The first apparatus determines a test result for the AI/ML model based on a comparison between the at least one predicted channel indicator and the at least one test channel indicator, the test result indicating whether the AI/ML model is validated.

    USER CONTEXT AWARE ML BASED CSI MEASUREMENT RELAXATION

    公开(公告)号:US20240147285A1

    公开(公告)日:2024-05-02

    申请号:US18490541

    申请日:2023-10-19

    CPC classification number: H04W24/10

    Abstract: Various techniques are provided for a method including communicating, by a user equipment (UE) to a network device, a message including a measurement relaxation request, receiving, by the UE from the network device, a message including one of a measurement relaxation approval or a measurement relaxation denial, in response to receiving the measurement relaxation approval predicting, by the UE, a measurement relaxation configuration using a machine learning model, communicating, by the UE to the network device, a message including the measurement relaxation configuration, receiving, by the UE from the network device, a message including a measurement relaxation acknowledgement, and reporting, by the UE to the network device, measurements based on the measurement relaxation configuration.

    METHOD AND SYSTEM FOR BEAM FAILURE MANAGEMENT

    公开(公告)号:US20230171836A1

    公开(公告)日:2023-06-01

    申请号:US17916658

    申请日:2020-04-01

    CPC classification number: H04W76/19 H04W64/00

    Abstract: A method and apparatus (100) for beam failure management for a user equipment (UE) (104) are described. A beam failure instance counter (BFI_counter) is determined, that is indicative of a number of consecutive beam failure instances occurred at the UE (104), at a time instance. A location of the UE (104) is determined at the time instance. A beam failure probability factor is determined, based at least on the location of the UE (104) at the time instance and the BFI_counter. The beam failure probability factor is indicative of a probability of occurrence of a beam failure at the location of the UE (104), after a plurality of further time instances. Further, the beam failure probability factor is compared with a beam failure threshold probability (beamF ailureThresholdProb). Thereafter, a beam failure is declared if the beam failure probability factor is higher than the beamFailureThresholdProb.

    BATTERY AWARE CARRIER ACTIVATION
    7.
    发明申请

    公开(公告)号:US20220182997A1

    公开(公告)日:2022-06-09

    申请号:US17532443

    申请日:2021-11-22

    Abstract: A method comprising receiving a first indication, from a terminal device, indicating that the terminal device is capable of supporting carrier aggregation, obtaining information regarding a battery level of the terminal device, estimating battery consumption per one carrier component, and based, at least partly, on the estimated battery consumption and information regarding the battery level, determining a number of additional carrier components to be activated for the terminal device.

    MODEL MONITORING FOR POSITIONING
    9.
    发明申请

    公开(公告)号:US20250056477A1

    公开(公告)日:2025-02-13

    申请号:US18792760

    申请日:2024-08-02

    Abstract: Example embodiments of the present disclosure relate to methods, devices, apparatuses and computer readable storage medium for a model monitoring for positioning, especially for assisted Artificial Intelligence/Machine Learning (AI/ML) positioning without measured ground truth (GT). The method comprises: determining, based on three or more transmission reception points, TRPs, at least one reference location associated with a positioning performance monitoring of a second apparatus; generating assistance data for monitoring a positioning performance at the second apparatus at least comprising at least one of: respective positioning measurement data of at least one positioning measurement type in the at least one reference location; respective monitoring metric associated with the at least one positioning measurement type in the at least one reference location; or the at least one reference location; and transmitting the assistance data to a second apparatus.

    DISTRIBUTED MACHINE LEARNING SOLUTION FOR ROGUE BASE STATION DETECTION

    公开(公告)号:US20240121678A1

    公开(公告)日:2024-04-11

    申请号:US18480318

    申请日:2023-10-03

    CPC classification number: H04W36/00833 H04W36/0085

    Abstract: An apparatus configured to: obtain an indication of partitions of a machine learning model corresponding to respective ones of the one or more groups; transmit, to the respective ones of the plurality of user equipments, a corresponding partition, of the partitions of the machine learning model; transmit, to the plurality of user equipments, an indication to record measurements for the first cell; receive, from at least one of the plurality of user equipments, at least one message regarding a handover failure, wherein the at least one message comprises a message generated using a first partition of the partitions of the machine learning model; and determine, with a second partition of the partitions of the machine learning model, whether the first cell is a rogue base station based, at least partially, on a plurality of detection reports.

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